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Hierarchical linear modelling is a statistical procedure that allows
for natural hierarchies within data to be taken into account in analysis.
A natural hierarchy, for example, might be patients, who are hierarchically
nested within general practices; the general practices are hierarchically
nested within health authorities. In this example, patients are said to
be at level 1 in the hierarchy, general practices at level 2, and health
authorities at level 3. Data pertaining to each level can be collected.
At level 1 this might be diet, smoking behaviour, blood pressure, and cholesterol;
at level 2 it might be the ratio of patients to general practitioners,
size of practice, prescription habits, practice facilities, and sociodemographics
of each practice; and level 3 data could be geographical region and funding.
Hierarchical linear modelling allows the researcher to explore how variables
at one level in the hierarchy influence variables at lower levels. For
example, are cholesterol levels best predicted by diet (level 1), the type
of practice (level 2), or the health authority region (level 3)? It allows
for the maximum power in the data to be used, mathematically incorporating
the inherent hierarchical nature of the data, and examines for influences
across levels within the hierarchy.

Examining the predictors of academic outcomes for indigenous Maori, Pacific and rural students admitted into medicine via two equity pathways: a retrospective observational study at the University of Auckland, Aotearoa New Zealand

Pilot study of the roles of personality, references, and personal statements in relation to performance over the five years of a medical degree * Commentary: How to derive causes from correlations in educational studies